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The science, technology, engineering, and math (STEM) education community is interested in using virtual reality (VR) to help students learn STEM knowledge. Prior research also provided evidence that VR learning can increase students’ motivation and learning achievement. However, it was not clear whether the effect of VR on learning was

The science, technology, engineering, and math (STEM) education community is interested in using virtual reality (VR) to help students learn STEM knowledge. Prior research also provided evidence that VR learning can increase students’ motivation and learning achievement. However, it was not clear whether the effect of VR on learning was partly from sensory novelty and whether the effectiveness was sustainable. This study was to satisfy the concern on the sustainability of VR STEM learning in instruction and address the research gaps in exploring the effect of VR on a STEM learning experience with a consideration of novelty.

The study used a mixed-methods experimental design and involved a three-session VR STEM learning intervention. The quantitative data was collected through the intervention by survey questionnaire, session quiz, and pre- and post-tests, while the interviews were taken after the intervention. The structural equation modeling method was used to explore the relationships among factors in the VR learning experience. Longitudinal quantitative comparisons were conducted with the multiple imputation method. Its purpose was to evaluate the changing magnitude of factors across sessions. After quantitative analysis, interview transcripts were analyzed. They were used to triangulate or provide context for understanding of quantitative results.

The results showed that motivation and engagement play a critical mediation role in an effective VR learning experience. While individuals’ psychological responses and motivation may significantly increase in a VR learning experience for novelty, the novelty effect may not steeply decrease when individuals are becoming familiar with the novelty. This phenomenon is more observable in a VR condition having a high degree of immersion and embodiment. In addition, novelty does not necessarily increase learning achievement. The increase of learning achievement is more dependent on a match between the learning content and the learning method. The embodied learning method is appropriate for instructing difficult knowledge and spatial knowledge. Reserving enough time for reflection is important to deep learning in a VR environment.
ContributorsHuang, Wen (Author) / Roscoe, Rod (Thesis advisor) / Johnson, Mina (Committee member) / Craig, Scotty (Committee member) / Arizona State University (Publisher)
Created2020
Description

Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on dee

Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on deep transcriptome sequences of adult skeletal muscle, lung, brain, and blood. The draft genome assembly for G. agassizii has a scaffold N50 length of 252 kbp and a total length of 2.4 Gbp. Genome annotation reveals 20,172 protein-coding genes in the G. agassizii assembly, and that gene structure is more similar to chicken than other turtles. We provide a series of comparative analyses demonstrating (1) that turtles are among the slowest-evolving genome-enabled reptiles, (2) amino acid changes in genes controlling desert tortoise traits such as shell development, longevity and osmoregulation, and (3) fixed variants across the Gopherus species complex in genes related to desert adaptations, including circadian rhythm and innate immune response. This G. agassizii genome reference and annotation is the first such resource for any tortoise, and will serve as a foundation for future analysis of the genetic basis of adaptations to the desert environment, allow for investigation into genomic factors affecting tortoise health, disease and longevity, and serve as a valuable resource for additional studies in this species complex.

Data Availability: All genomic and transcriptomic sequence files are available from the NIH-NCBI BioProject database (accession numbers PRJNA352725, PRJNA352726, and PRJNA281763). All genome assembly, transcriptome assembly, predicted protein, transcript, genome annotation, repeatmasker, phylogenetic trees, .vcf and GO enrichment files are available on Harvard Dataverse (doi:10.7910/DVN/EH2S9K).

ContributorsTollis, Marc (Author) / DeNardo, Dale F (Author) / Cornelius, John A (Author) / Dolby, Greer A (Author) / Edwards, Taylor (Author) / Henen, Brian T. (Author) / Karl, Alice E. (Author) / Murphy, Robert W. (Author) / Kusumi, Kenro (Author)
Created2017-05-31